Hypothesis testing is very essential in statistical analysis. It is quite imperative to state both a null hypothesis and an alternative hypothesis when conducting a hypothesis test because the hypotheses are mutually exclusive and if one statement is true then the other is proven as false. According to Mirabella (2011, p. 4-1) states that, “When we have a theory about a parameter (the average is…,the proportion is….,etc), we can test that theory via a hypothesis test.” Therefore, that is what we have used to determine if the average age of Whatsamatta U MBA students is less than 45. We have to conduct a one sample hypothesis test to prove if we can accept or reject the null hypothesis. In order to determine what decision rule, which is the statement that tells under which condition to reject the null hypothesis, to propose we must first determine if we are instituting a upper-tailed, lower-tailed, or two-tailed test. There are many steps used when initiating a hypothesis test. First and foremost it is vital to specify the null and alternative hypothesis, next we have to determine a significance level that is tolerable such as 0.05 which is your tolerance for error, and last we have to calculate the statistic that is comparable to the parameters set by the null hypothesis.
Additionally, when utilizing hypothesis testing, we are attempting to determine whether or not to accept or reject the null hypothesis. Furthermore, we have to determine if there is enough
So, we should reject the null hypothesis H0. At a 0.05 level of significance level, we conclude that there is a significant difference between the average height for females and the average height for the males.
Hypothesis testing and development provides a baseline for taking ideas or theories that were initially created by another person in regards to the markets, economy, or investing and then determining if the
Hypothesis is typically used in quantitative research only. Moreover, when a question poses an inquiry on the relationship between two variables, a hypothesis is a statement declarative in nature of the relationship between different variables (Pajares 2007). A researcher chooses whether to use a question or a hypothesis depending on the purpose of the research, its objectives, the methodology for the research and the preference of the audience to receive the research. A researcher must be able to interpret the final outcome with reference to the research questions or the hypothesis used (Pajares 2007). A research requires a minimum of two hypotheses namely a null and an alternative hypothesis.
Decision Rule: The calculated test statistic of -3.024 does fall in the rejection region of z<-1.645, therefore I would reject the null and say there is sufficient evidence to indicate mu<50.
Testing allows the p-value that represents the probability showing that results are unlikely to occur by chance. A p-value of 5% or lower is statistically significant. The p value helps in minimizing Type I or Type II errors in the dataset that can often occur when the p value is more than the significance level. The p value can help in stopping the positive and negative correlation between the dataset to reject the null hypothesis and to determine if there is statistical significance in the hypothesis. Understanding the p value is very important in helping researchers to determine the significance of the effect of their experiment and variables for other researchers
Select one (1) project from your working or educational environment that you would use the hypothesis test technique. Next, propose the hypothesis structure (e.g., the null hypothesis, data collection process, confidence interval, test statistics, reject or not reject the decision, etc.) for the business process of the selected project. Provide a rationale for your response.
Create a research hypothesis tested using a one-tailed test and a research hypothesis tested using a two-tailed test.
When you perform a test of hypothesis, you must always use the 4-step approach: i. S1:the “Null” and “Alternate” hypotheses, ii. S2: calculate value of the test statistic, iii. S3: the level of significance and the critical value of the statistic, iv. S4: your decision rule and the conclusion reached in not rejecting or rejecting the null hypothesis. When asked to calculate p–value, S5, relate the p-value to the level of significance in reaching your conclusion.
A hypothesis is tested through an experiment. To have effective testing, the experiment must have controlled variables, one control (without independent variable), and an experimental group.
Explain how the data collected will provide the data necessary to support or negate the hypothesis or proposition
To test the null hypothesis, if the P-Value of the test is less than 0.05 I will reject the null hypothesis.
“Hypothesis testing is a decision-making process for evaluating claims about a population” (Bluman, 2013, p. 398). This process is used to determine if you will accept or reject the hypothesis. The claim is that the bottles contain less than 16 ounces. The null hypothesis is the soda bottles contain 16 ounces. The alternative hypothesis is the bottles contain less than 16 ounces. The significance level will be 0.05. The test method to be used is a t-score. The test statistic is calculated to be -11.24666539 and the P-value is 1.0. The P-value is the probability of observing a sample statistic as extreme as the test statistic, assuming the null hypothesis is true. The T Crit value is 1.69912702. The calculations show there is enough evidence to support the claim that the soda bottles do
Conclusion : Reject the null hypothesis. The sample provide enough evidence to support the claim that mean is significantly different from 12 .
The research methodology is to divide the teams by their salary means. Then the win means will be compared to determine if there’s a significant difference in production of high salary teams and lower salary teams. 1, teams with larger payrolls, will be defined as teams with payrolls that are average or above average (see chart team A) and 2, teams with smaller payrolls, will be defined as teams with less than average payrolls (see chart team B).
The null hypothesis suggests that there is no difference between the means of the three samples, while the claim in the alternative hypothesis suggests that at least one mean is different.